An experiment with ontology mapping using concept similarity

This paper describes a system for automatically mapping between concepts in different ontologies. The motivation for the research stems from the Diogene project, in which the project's own ontology covering the ICT domain is mapped to external ontologies, in order that their associated content can automatically be included in the Diogene system. An approach involving measuring the similarity of concepts is introduced, in which standard Information Retrieval indexing techniques are applied to concept descriptions. A matrix representing the similarity of concepts in two ontologies is generated, and a mapping is performed based on two parameters: the domain coverage of the ontologies, and their levels of granularity. Finally, some initial experimentation is presented which suggests that our approach meets the project's unique set of requirements

Exploring The Value Of Folksonomies For Creating Semantic Metadata

Finding good keywords to describe resources is an on-going problem: typically we select such words manually from a thesaurus of terms, or they are created using automatic keyword extraction techniques. Folksonomies are an increasingly well populated source of unstructured tags describing web resources. This paper explores the value of the folksonomy tags as potential source of keyword metadata by examining the relationship between folksonomies, community produced annotations, and keywords extracted by machines. The experiment has been carried-out in two ways: subjectively, by asking two human indexers to evaluate the quality of the generated keywords from both systems; and automatically, by measuring the percentage of overlap between the folksonomy set and machine generated keywords set. The results of this experiment show that the folksonomy tags agree more closely with the human generated keywords than those automatically generated. The results also showed that the trained indexers preferred the semantics of folksonomy tags compared to keywords extracted automatically. These results can be considered as evidence for the strong relationship of folksonomies to the human indexer’s mindset, demonstrating that folksonomies used in the del.icio.us bookmarking service are a potential source for generating semantic metadata to annotate web resources

Terminology server for improved resource discovery: analysis of model and functions

This paper considers the potential to improve distributed information retrieval via a terminologies server. The restriction upon effective resource discovery caused by the use of disparate terminologies across services and collections is outlined, before considering a DDC spine based approach involving inter-scheme mapping as a possible solution. The developing HILT model is discussed alongside other existing models and alternative approaches to solving the terminologies problem. Results from the current HILT pilot are presented to illustrate functionality and suggestions are made for further research and development

The Neuroscience Information Framework: A Data and Knowledge Environment for Neuroscience

With support from the Institutes and Centers forming the NIH Blueprint for Neuroscience Research, we have designed and implemented a new initiative for integrating access to and use of Web-based neuroscience resources: the Neuroscience Information Framework. The Framework arises from the expressed need of the neuroscience community for neuroinformatic tools and resources to aid scientific inquiry, builds upon prior development of neuroinformatics by the Human Brain Project and others, and directly derives from the Society for Neuroscience’s Neuroscience Database Gateway. Partnered with the Society, its Neuroinformatics Committee, and volunteer consultant-collaborators, our multi-site consortium has developed: (1) a comprehensive, dynamic, inventory of Web-accessible neuroscience resources, (2) an extended and integrated terminology describing resources and contents, and (3) a framework accepting and aiding concept-based queries. Evolving instantiations of the Framework may be viewed at http://nif.nih.gov, http://neurogateway.org, and other sites as they come on line

A knowledge-based approach to information extraction for semantic interoperability in the archaeology domain

The paper presents a method for automatic semantic indexing of archaeological grey-literature reports using empirical (rule-based) Information Extraction techniques in combination with domain-specific knowledge organization systems. Performance is evaluated via the Gold Standard method. The semantic annotation system (OPTIMA) performs the tasks of Named Entity Recognition, Relation Extraction, Negation Detection and Word Sense disambiguation using hand-crafted rules and terminological resources for associating contextual abstractions with classes of the standard ontology (ISO 21127:2006) CIDOC Conceptual Reference Model (CRM) for cultural heritage and its archaeological extension, CRM-EH, together with concepts from English Heritage thesauri and glossaries.Relation Extraction performance benefits from a syntactic based definition of relation extraction patterns derived from domain oriented corpus analysis. The evaluation also shows clear benefit in the use of assistive NLP modules relating to word-sense disambiguation, negation detection and noun phrase validation, together with controlled thesaurus expansion.The semantic indexing results demonstrate the capacity of rule-based Information Extraction techniques to deliver interoperable semantic abstractions (semantic annotations) with respect to the CIDOC CRM and archaeological thesauri. Major contributions include recognition of relevant entities using shallow parsing NLP techniques driven by a complimentary use of ontological and terminological domain resources and empirical derivation of context-driven relation extraction rules for the recognition of semantic relationships from phrases of unstructured text. The semantic annotations have proven capable of supporting semantic query, document study and cross-searching via the ontology framework

Ontologies on the semantic web

As an informational technology, the World Wide Web has enjoyed spectacular success. In just ten years it has transformed the way information is produced, stored, and shared in arenas as diverse as shopping, family photo albums, and high-level academic research. The “Semantic Web” was touted by its developers as equally revolutionary but has not yet achieved anything like the Web’s exponential uptake. This 17 000 word survey article explores why this might be so, from a perspective that bridges both philosophy and IT

enabling a research data management beyond data heterogeneity

A primary goal of a research infrastructure for data management should be to enable efficient data discovery and integration of heterogeneous data. The German Federation for Biological Data (GFBio) was envisioned by this goal. The basic component, that enables such interoperability and serves as a backbone for such a platform, is the GFBio Terminology Service (GFBio TS). It acts as a semantic platform for accessing, developing and reasoning over terminological resources within the biological and environmental domain. A RESTful API gives access to these terminological resources in a uniform way regardless of their degree of complexity and whether they are internally stored or externally accessed through their web services. Additionally, a set of widgets with an intrinsic API connection are made available for an easy integration in applications and web interfaces. Based on the requirements of the GFBio partners, we describe the added value that is provided by the GFBio Terminology Service with practical scenarios but also, what challenges we still face. We conclude by describing our current activities and future developments

A pilot investigation of Information Extraction in the semantic annotation of archaeological reports

The paper discusses a prototype investigation of semantic annotation, a form of metadata assigning conceptual entities to textual instances; in the case of archaeological grey literature. The use of Information Extraction (IE), a Natural Language Processing (NLP) technique, is central to the annotation process while the use of Knowledge Organization System (KOS) is explored for the association of semantic annotation with both ontological and terminological references. The annotation process follows a rule-based information extraction approach using the GATE NLP toolkit, together with the CIDOC CRM ontology, its CRM-EH archaeological extension and English Heritage thesauri and glossaries. Results are reported from an initial evaluation, which suggest that these information extraction techniques can be applied to archaeological grey literature reports. Further work is discussed drawing on the evaluation and consideration of the characteristics of the archaeology domain. Copyright © 2012 Inderscience Enterprises Ltd